Inference of Causal Effects when Control Variables are Unknown

• URL: http://arxiv.org/abs/2012.08154v2
• Date: Fri, 19 Feb 2021 14:59:07 GMT
• Title: Inference of Causal Effects when Control Variables are Unknown
• Abstract summary: We propose a method for inferring average causal effects when all potential confounders are observed. When the data-generating process belongs to the class of acyclical structural causal models, we prove that the method yields valid confidence intervals.
• Score: 16.244541005112747
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Conventional methods in causal effect inferencetypically rely on specifying a valid set of control variables. When this set is unknown or misspecified, inferences will be erroneous. We propose a method for inferring average causal effects when all potential confounders are observed, but thecontrol variables are unknown. When the data-generating process belongs to the class of acyclical linear structural causal models, we prove that themethod yields asymptotically valid confidence intervals. Our results build upon a smooth characterization of linear directed acyclic graphs. We verify the capability of the method to produce valid confidence intervals for average causal effects using synthetic data, even when the appropriate specification of control variables is unknown.

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